Cloud migration and modernisation

Moving to the cloud is the easy part. Landing somewhere better than where you started is the work.

We migrate workloads from on-premise data centres, colocation, or another cloud provider - deciding case by case what should be rehosted untouched, what deserves refactoring, and what should simply be retired.

Discovery first

Most migrations run into trouble because nobody mapped the dependencies. A service moves, and three things nobody knew about it stop working.

We start by inventorying what you actually run: traffic patterns, data volumes, integration points, licensing, and the undocumented cron job on a server nobody wants to touch. That produces a wave plan - which workloads move together, in what order, and what each cutover needs.

Not everything deserves refactoring

Lift-and-shift gets criticised, and sometimes unfairly. A stable internal application with predictable load and no growth plans does not need to become microservices. Rehost it, rightsize it, and spend the engineering budget where it changes something.

We reserve refactoring for workloads where the cloud genuinely unlocks value - anything with spiky demand, anything you deploy often, anything where the current architecture is the reason you cannot ship.

Cutover without the weekend

Database migration is where most timelines slip. We use replication-based approaches that keep source and target in sync so the actual switch is minutes rather than hours, with a tested rollback at every step.

Where a hybrid state has to persist - because a licence, a regulator or a mainframe says so - we design that connectivity properly rather than treating it as a temporary hack that lives for three years.

Not every workload needs a hyperscaler

AWS, Azure and GCP are the default for good reasons, but they are not the only option. A predictable workload with modest scale requirements is often better served by DigitalOcean or Linode, where the bill is simpler and there is far less platform to operate.

We size the target to the workload rather than defaulting to the biggest name. If a hyperscaler’s breadth genuinely earns its cost and complexity for you, we say so. If it doesn’t, we say that too.